Title :
A recursive estimation of the condition number in the RLS algorithm [adaptive signal processing applications]
Author :
Benesty, Jacob ; Gänsler, Tomas
Author_Institution :
INRS-EMT, Quebec Univ., Montreal, Que., Canada
Abstract :
The recursive least-squares (RLS) algorithm is one of the most popular adaptive algorithms in the literature. This is due to the fact that it is easily derived and exactly solves the normal equations. In this paper, we present a very efficient way to recursively estimate the condition number of the input signal covariance matrix by utilizing fast versions of the RLS algorithm. We also quantify the misalignment of the RLS algorithm with respect to the condition number.
Keywords :
adaptive signal processing; covariance matrices; least squares approximations; recursive estimation; RLS algorithm condition number misalignment; adaptive filters; condition number recursive estimation; input signal covariance matrix; recursive least-squares algorithm; Acoustic applications; Adaptive algorithm; Adaptive filters; Covariance matrix; Equations; Jacobian matrices; Least squares approximation; Recursive estimation; Resonance light scattering; Speech;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
Print_ISBN :
0-7803-8874-7
DOI :
10.1109/ICASSP.2005.1415936
